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608 results for “ensembles”
Ensemble calculations of "Tx75p" from EURO-CORDEX data for Europe
<p><strong>Climate Index: </strong>Tx75p</p> <p><strong>Definition:</strong> Number of days that the daily maximum temperature is above the 75th percentile of daily maximum temperatures during the warm season of April-September of the period 1971-2000.</p> <p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p> <p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p> <p>The bias-corrected EURO-CORDEX climate model simulations used are:</p> <ul> <li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li> <li>DMI-HIRHAM5/ICHEC-EC-EARTH</li> <li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li> <li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li> </ul>
Ensemble projections elucidate effects of uncertainty in terrestrial nitrogen limitation on future carbon uptake
<p>Simulation output as described in Meyerholt, J., Sickel K., and Zaehle, S., (2020), Ensemble projections elucidate effects of uncertainty in terrestrial nitrogen limitation on future carbon uptake, Global Change Biology, doi:10.1111/gcb.15114</p> <p>Data in the file <a href="https://zenodo.org/api/files/62e7c4a2-96e9-46b8-810f-c4c3c24b06c0/ocn4magicc_carbon_model.nc?versionId=cb25734e-52ce-42fa-b238-9b7192baf4dd">ocn4magicc_carbon_model.nc</a> describe the carbon-only version of the model, <a href="https://zenodo.org/api/files/62e7c4a2-96e9-46b8-810f-c4c3c24b06c0/ocn4magicc_carbon_model.nc?versionId=cb25734e-52ce-42fa-b238-9b7192baf4dd">ocn4magicc_nitrogen_models.nc </a>describe the carbon-nitrogen model outputs.</p>
customized gtf file from Ensembl version 93 mm10
<p>The gtf from Ensembl version 93 was filtered to remove readthrough transcripts and all non-coding transcripts from a protein-coding gene. In addition, all genes with the same gene name which overlaps were merged under the same gene to avoid ambiguous reads.</p>
customized gtf file from Ensembl version 92 mm10
<p>The gtf from Ensembl version 92 was filtered to remove readthrough transcripts and all non-coding transcripts from a protein-coding gene.</p>
GFDL-FLOR Large Ensemble Arctic Sea Ice Data
<p>This upload contains Arctic sea ice data from the GFDL-FLOR Large Ensemble and related data analysis code, as published in Bushuk et al. (2020). See readme.txt for a description of the datasets and code.</p> <p>Reference: Bushuk, M., M. Winton, D. Bonan, E. Blanchard-Wrigglesworth, T. Delworth, 2020: A mechanism for the Arctic sea ice spring predictability barrier, Geophysical Research Letters, in press.</p>
Cinque Terre Flooding - 25 Oct 2011 - MOLOCH Ensemble Reforecasts
<p>Ensemble-based reforecasts using the MOLOCH model</p> <p>Variable: Total precipitation [kg/m^2]</p> <p>Starting dates: 2011/10/23 00 UTC, 2011/10/23 12 UTC, 2011/10/24 00 UTC, 2011/10/24 12 UTC</p> <p>Ending date: 2011/10/26 00 UTC</p>
Cinque Terre Flooding - 25 Oct 2011 - Meso-NH Ensemble Reforecasts
<p>Ensemble-based reforecasts using the Meso-NH model</p> <p>Variable: Total precipitation [kg/m^2]</p> <p>Starting dates: 2011/10/23 00 UTC, 2011/10/23 12 UTC, 2011/10/24 00 UTC, 2011/10/24 12 UTC</p> <p>Ending date: 2011/10/26 00 UTC</p>
Hydro-CH2018-Runoff ensemble
<p>The Hydro-CH2018-Runoff ensemble consists of daily runoff simulations for 93 catchments in Switzerland for the period 1981-2099 run with the newest climate change scenarios for Switzerland (CH2018). The simulations were performed with the hydrological modelling system PREVAH on soundly calibrated and validated catchments.</p> <p>The zip-file includes 4 folders including data: <br>-- daily: daily simulations per catchment and RCP scenario with columns for each model in the respective RCP scenario<br>-- monthly: monthly means of daily simulations per catchment and RCP scenario with columns for each model in the respective RCP scenario<br>-- seasonal: seasonal means (winter-DJF, spring-MAM, summer-JJA, autumn-SON) of daily simulations per catchment and RCP scenario with columns for each model in the respective RCP scenario<br>-- yearly: yearly means of of daily simulations per catchment and RCP scenario with columns for each model in the respective RCP scenario</p> <p>The data is accompanied by a list of models and corresponding RCP scenarios, a list of catchment IDs and corresponding gauging station names, and a short readme with the filename convention. </p> <p>There exist daily, monthly, seasonal, and yearly data for each catchment for<br>12 GCM-RCM chains under RCP2.6 scenario<br>25 GCM-RCM chains under RCP4.5 scenario<br>30 GCM-RCM chains under RCP8.5 scenario </p> <p>A paper describing the dataset and the underlying methods has been published: Muelchi, R., Rössler, O., Schwanbeck, J., Weingartner, R., Martius, O. (2021): An ensemble of daily simulated runoff data (1981-2099) under climate change conditions for 93 catchments in Switzerland (Hydro-CH2018-Runoff ensemble). Geoscience Data Journal, <a href="https://doi.org/10.1002/gdj3.117">https://doi.org/10.1002/gdj3.117</a></p>
Data from: Homogenisation of carnivorous mammal ensembles caused by global range reductions of large-bodied hypercarnivores during the late Quaternary
Carnivorous mammals play crucial roles in ecosystems by influencing prey densities and behaviour, and recycling carrion. Yet, the influence of carnivores on global ecosystems has been affected by extinctions and range contractions throughout the Late Pleistocene and Holocene (~130 000 years ago to the current). Large-bodied mammals were particularly affected, but how dietary strategies influenced species' susceptibility to geographic range reductions remains unknown. We investigated 1) the importance of dietary strategies in explaining range reductions of carnivorous mammals (≥5% vertebrate meat consumption), and 2) differences in functional diversity of continental carnivore ensembles by comparing current, known ranges to current, expected ranges under a present-natural counterfactual scenario. The present-natural counterfactual estimates current mammal ranges had modern humans not expanded out of Africa during the Late Pleistocene and were not a main driver of extinctions and range contractions, alongside changing climates. Ranges of large-bodied hypercarnivorous mammals are currently smaller than expected, compared to smaller-bodied carnivorous mammals that consume less vertebrate meat. This resulted in consistent differences in continental functional diversity, whereby current ensembles of carnivorous mammals have undergone homogenisation through structural shifts towards smaller-bodied insectivorous and herbivorous species. The magnitude of ensemble structural shift varied among continents, with Australia experiencing the greatest difference. Weighting functional diversity by species' geographic range sizes caused a three-fold greater shift in ensemble centroids than when using presence-absence alone. Conservation efforts should acknowledge current reductions in the potential geographic ranges of large-bodied hypercarnivores and aim to restore functional roles in carnivore ensembles, where possible, across continents.
Data release for 'Ensemble Forecasting of Major Solar Flares: Methods for Combining Models'
<p>This is a release of the data that were used for validation in the paper 'Ensemble Forecasting of Major Solar Flares: Methods for Combining Models' by J. A. Guerra, S. A. Murray, D. S. Bloomfield, and P. T. Gallagher, that has been submitted to the Journal of Space Weather and Space Climate.</p> <p> </p> <ul> </ul> <p>The naming scheme for the files is in the format:</p> <pre><code>class_type_metric.dat</code></pre> <ul> <li>'class' denotes whether the forecast is for M- or X- class flares.</li> <li>'type' is what kind of forecast, i.e., the original ensemble members, an ensemble created from probabilistic validation metrics, or an ensemble created from categorical validation metrics.</li> <li>'metric' specifies the metric used to create the ensemble in the case of 'probabilistic' or 'categorical' types as above (see paper for further details), or in the case of the original ensemble members the name of the operational forecasting method.</li> </ul> <p> </p> <p>The data files are in the format:</p> <pre><code>obs,prob</code></pre> <ul> <li>'obs' denotes whether or not a flare was observed within 24 hours of the forecast issue time (1 for yes and 0 for no).</li> <li>'prob' gives the probabilistic forecast value (between 0.0 and 1.0).</li> </ul> <p> </p> <p>These data files can easily be read into the <a href="https://cran.r-project.org/web/packages/verification/verification.pdf">R verification package</a> to replicate the results presented in the paper.</p>
Summary wind statistics from NEWA WRF mesoscale ensemble
<p>These files contain summary statistics from the NEWA WRF mesoscale ensemble for a Northern European domain.</p> <p>Each netCDF file includes:<br> - Mean wind speed (50, 75, 100 and 150 m AGL)<br> - Surface mean air density<br> - Surface static fields (latitude, longitude, surface elevation and surface roughness)<br> - Wind speed and direction frequency distribution (100 m AGL)</p> <p>The WRF simulation setup and parameterizations correspond to those in the README.ensemble file.</p>
An Ensemble Approach to Automatic Structuring of Radiology Reports -- Dataset
<p>This dataset contains the annotations for 100 radiology reports from MIMIC-III dataset. The annotations use the BRAT format and this dataset only provides the annotations (not the original radiology reports). In order to view them on BRAT, you need to request and download the original text from MIMIC-III website. Please note that the file names are the IDs for each radiology report in the MIMIC-III dataset.</p>
input and TS ensemble for "Converging experimental and computational views of the knotting mechanism of the smallest knotted protein"
<p>PLUMED input and TS ensemble for "Converging experimental and computational views of the knotting mechanism of the smallest knotted protein"</p>
Cluster configurations of the Hegselmann-Krause model on network ensembles
<p>This is the raw data underlying the results of the preprint [arxiv:2102.10910](https://arxiv.org/abs/2102.10910).</p> <p> </p> <p>## Data</p> <p>For each measured combination of the confidence and system size, there is one gzipped<br> file. For different ensembles, we collected data in different ranges and quality.<br> The paramters are:</p> <p>* Number of samples `m` per parameter combination<br> * Range `r` of confidences epsilon<br> * Distances `d` between values of epsilon (basically the resolution of the data)<br> * Largest size `N_max`</p> <p>The single files follow a naming scheme of `n{N}_e{epsilon}.cluster.dat.gz`, where<br> `{N}` signals the system size of the simulation and `{epsilon}` is the confidence<br> value of the simulation (without a decimal point, i.e., `0050` corresponds to `epsilon = 0.050`).<br> The sizes `N` are usually powers of two (or for the lattices, perfect squares close to powers of two).</p> <p>We present the data for each ensemble in one archive.</p> <p><br> * Fully connected `full.tar`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 262144`<br> * Barabasi Albert with a mean degree of 4 `BA4.tar`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 32768`<br> * Barabasi Albert with a mean degree of 10 `BA10.tar`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 65536`<br> * Square lattice with first nearest neighbors `lat1.tar`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 16384`<br> * Square lattice with second nearest neighbors `lat2.tar`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 16384`<br> * Square lattice with third nearest neighbors `lat3.tar`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 65536`<br> * Square lattice with fourth nearest neighbors `lat4.tar`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 65536`<br> * Square lattice with third nearest neighbors and 1% rewired edges `lat3_ws.tar`<br> * `m = 1000`, `r = [0.0, 0.3]`, `d = 0.001`, `N_max = 16384`<br> * connected Erdos Renyi with mean degree of 10 `ER10.tar`<br> * `m = 1000`, `r = [0.0, 0.3]`, `d = 0.002`, `N_max = 32768`</p> <p> </p> <p>## Data format</p> <p>Each final state is encoded as three lines:</p> <p>* The convergence time is a single integer with a line prefix '# sweeps: '<br> * The positions of all clusters in opinion space with a line prefix '# ' (unsorted)<br> * The number of agents in each of the clusters without a line prefix</p> <p> </p> <p>## Python example for reading the format</p> <p>An example script, which visualizes the S vs eps graph for the largest size of the fully connected<br> case, with a function to read this format is given in `example.py`.</p>
Application of an ensemble earthquake rate model in Italy, considering seismic catalogs and fault moment release - Models and Catalogs
<p>Models and Catalogs for "Application of an ensemble earthquake rate model in Italy, considering seismic catalogs and fault moment release" , Murru et al. 2020</p>
Accelerating future mass loss of Svalbard glaciers from a multi-model ensemble
<p>This dataset contains the data behind the figures 2-13 and A1-A4 in the article "Accelerating future mass loss of Svalbard glaciers from a multi-model ensemble", which will be published in Journal of Glaciology.</p>
Data from: How does spatial resolution affect model performance? A case for ensemble approaches for marine benthic mesophotic communities
Aim: To investigate how changing grid size can alter model predictions of the distribution of mesophotic taxa and how it affects different modelling methods. Location: Ningaloo Marine Park, Western Australia. Taxon: Benthic mesophotic taxa: corals, macroalgae, and sponges. Methods: We determined the distributions of the major benthic taxonomic groups: corals, macroalgae, and sponges, using a number of modelling techniques and an ensemble using the 'sdm' R package. A range of grid sizes were used (10 m, 50 m, 100 m, and 250 m) to identify how model predictions were altered. Models were evaluated using the area under the curve of a receiver operator characteristic plot (AUC) and the true skill statistic (TSS) using a spatially independent dataset. Results: Grid size had a large effect on model performance across the taxonomic groups. Model outputs were compared to null surfaces and 88.8% of models performed significantly better than null. Distribution of corals was best predicted using the finest grid size (10 m) regardless of modelling method, although a model ensemble produced the best results (AUC = 0.80, TSS = 0.52). Macroalgae and sponges were better predicted at coaster grids sizes (250 m). Again, ensembles performed well for both macroalgae (AUC = 0.83, TSS = 0.63) and sponges (AUC = 0.88, TSS = 0.66). Model ensembles maintained high accuracy across grid sizes and were consistently the best, or second-best, performing method. Main Conclusions: This study has shown how grid size should be considered when producing distribution models. Identifying the most relevant grid size and being aware of the influence it may have will provide more accurate predictions of the distributions of taxa. Ensemble methods maintained good performance across scenarios and thus provide a useful tool for conservation and management especially where single modelling methods showed high levels of variability.
Data from: Ecomorphology of the African felid ensemble: the role of the skull and postcranium in determining species segregation and assembling history
Morphology of extant felids is regarded as highly conservative. Most previous studies have focussed on skull morphology, so a vacuum exists about morphofunctional variation in postcranium and its role in structuring ensembles of felids in different continents. The African felid ensemble is particularly rich in ecologically specialized felids. We studied the ecomorphology of this ensemble using 31 cranial and 93 postcranial morphometric variables measured in 49 specimens of all 10 African species. We took a multivariate approach controlling for phylogeny, with and without body size correction. Postcranial and skull + postcranial analyses (but not skull-only analyses) allowed for a complete segregation of species in morphospace. Morphofunctional factors segregating species included body size, bite force, zeugopodial lengths and osteological features related to parasagittal leg movement. A general gradient of bodily proportions was recovered: lightly built, long-legged felids with small heads and weak bite forces vs. the opposite. Three loose groups were recognized: small terrestrial felids, mid-to-large sized scansorial felids and specialized Acinonyx jubatus and Leptailurus serval. As predicted from a previous study, the assembling of the African felid ensemble during the Plio-Pleistocene occurred by the arrival of distinct felid lineages that occupied then vacant areas of morphospace, later diversifying in the continent.
Data from: Ensemble approach for potential habitat mapping of invasive Prosopis in Turkana, Kenya
Aim: Prosopis spp. are an invasive alien plant species native to the Americas and well adapted to thrive in arid environments. In Kenya, several remote‐sensing studies conclude that the genus is well established throughout the country and is rapidly in‐ vading new areas. This research aims to model the potential habitat of Prosopis spp. by using an ensemble model consisting of four species distribution models. Furthermore, environmental and expert knowledge‐based variables are assessed. Location: Turkana County, Kenya. Methods: We collected and assessed a large number of environmental and expert knowl‐ edge‐based variables through variable correlation, collinearity, and bias tests. The varia‐ bles were used for an ensemble model consisting of four species distribution models: (a) logistic regression, (b) maximum entropy, (c) random forest, and (d) Bayesian networks. The models were evaluated through a block cross‐validation providing statistical measures. Results: The best predictors for Prosopis spp. habitat are distance from water and built‐up areas, soil type, elevation, lithology, and temperature seasonality. All species distribution models achieved high accuracies while the ensemble model achieved the highest scores. Highly and moderately suitable Prosopis spp. habitat covers 6% and 9% of the study area, respectively. Main conclusions: Both ensemble and individual models predict a high risk of continued invasion, confirming local observations and conceptions. Findings are valuable to stake‐ holders for managing invaded area, protecting areas at risk, and to raise awareness.
Video figure: Exploring percussive gesture on iPads with Ensemble Metatone
<p>This video figure shows Ensemble Metatone, a group of percussionist, exploring the possibilities of ensemble performance with two iPad apps, MetaTravels and MetaLonsdale. This process of rehearsal as research was subjected to qualitative analysis to characterise the performers touch gestures and ensemble interactions.</p> <p>The abstract for the paper that accompanied this video is below:</p> <p>Percussionists are unique among western classical instrumentalists in that their artistic practice is defined by an approach to interaction rather than their instruments. While percussionists are accustomed to exploring non-traditional objects to create music, these objects have yet to encompass touch-screen computing devices to any great extent. The proliferation and popularity of these devices now presents an opportunity to explore their use in combining computer-generated sound together with percussive interaction in a musical ensemble.</p> <p>This paper examines Ensemble Metatone, a group formed to explore the "infiltration" of iPad-based musical instruments into a free-improvisation percussion ensemble. We discuss the design approach for two different iPad percussion instruments and the methodology for exploring them with the group over a series of rehearsals and performances. Qualitative analysis of discussions throughout this process shows that the musicians developed a vocabulary of gestures and musical interactions to make musical sense of these new instruments.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.